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Oil and gas engineering (OGE)

作者： Svalova, A., I Stishenko, P., V Omsk State Tech Univ 11 Mira Pr Omsk 644050 Russia

ISBN: (纸本)9781510809888

This paper deals with the atom energy mean value of the platinum nanoparticles of various size (from 300 to 3000 atoms that approximately corresponds to the transition threshold from the icosahedral structure to the face-centered cubic one) on the face-centered cubic lattice and on the five-fold symmetry one, with the application of the modified metropolis algorithm based on the Monte-Carlo method. When calculating the energy the quantum Sutton-Chen potential is used. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： metropolis algorithm platinum nanoparticles quantum Sutton-Chen potentials

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Sampling from multitarget Bayesian posteriors for random sets via jump-diffusion processes

Sampling from multitarget Bayesian posteriors for random set...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XII

作者： Lanterman, AD Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819449563

Bayesian multitarget tracking is an inherently nonlinear problem. Even when the state models and sensor noise associated with individual targets and observations is Gaussian, the "true" data likelihood, as formulated within the framework of finite-set statistics, is non-Gaussian. Missed detections and false alarms, combined with the fact that targets may enter and leave the scene at random times, complicate matters further. The resulting Bayesian posterior is analytically foreboding, and many conventional estimators are not even defined. We propose an algorithm for generating samples from the posterior based on jump-diffusion processes. When discretized for computer implementation, the jump-diffusion method falls into the general class of Markov chain Monte Carlo methods. The diffusions refine estimates of continuous parameters, such as positions and velocities, whereas the jumps are responsible for major discrete changes, such as adding and removing targets. Jump-diffusion processes have been previously applied to performing automatic target recognition in infrared images and tracking multiple targets using raw narrowband sensor array and high-resolution range profile data. Here, we apply jump-diffusion to the more traditional class of target tracking problems where raw sensor data is preprocessed into reports, but the report-to-target association is unknown. Our formulation maintains the flavor of other recent work employing finite-set statistics, in that no attempts to explicitly associate specific reports with specific targets are needed.

关键词： multitarget tracking jump-diffusion processes finite-set statistics Markov chain Monte Carlo metropolis algorithm

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Ultra-fast calculation scheme for small-angle multi-scattering problems

Ultra-fast calculation scheme for small-angle multi-scatteri...

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12th Advanced Laser Technoloies 2004 Conference

作者： Shuvalov, VV Shutov, IV Tret'akov, EV Voronov, AV Moscow MV Lomonosov State Univ Moscow 119899 Russia

A new ultra-fast (increasing the calculation rate about 9 orders and more) calculation scheme, enabling one to solve small-angle multi-scattering problems for large (size about 1000 scattering lengths and more) object... 详细信息

ISBN: (纸本)0819458473

关键词： diffusion optical tomography multiple small-angle scattering path-integration technique metropolis algorithm ultra-fast calculations

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Comparison between Bayesian Method and LSE in Estimating MTBF of NC Machine Tools

Comparison between Bayesian Method and LSE in Estimating MTB...

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International Conference on Computer Science and Mechanical Automation (CSMA 2015)

作者： Kan, Yingnan Zhu, Xiaocui Wang, Lihui Xu, Binbin Yang, Zhaojun Li, Hongzhou Jilin Univ Coll Mech Sci & Engn Changchun Peoples R China

ISBN: (纸本)9781467391665

Aiming at the large bias of LSE (Least Squares Estimation) in estimating MTBF (mean time between failures) under a small sample of data, a Bayesian MTBF estimating method is proposed for NC (numerical control) machine tools. To solve difficulty in directly presenting the prior distributions of Weibull parameters, an expert-judgment method which incorporates prior information is developed to indirectly obtain Weibull parameters' prior distributions. Aiming at the problem that analytic solutions to Weibull parameters' posterior distributions and estimators are impossible to obtain, a metropolis algorithm is developed. The iteration procedure of the algorithm is presented;the posterior distribution of each parameter is simulated;and the parameter estimators and MTBF are obtained. Given the actual MTBF as standard value, the proposed method and LSE are applied to the same real case respectively. The results indicate that when sample size n <= 10, relative errors of the proposed method lie between 4.43% and 7.19%, which are smaller than those of LSE. The proposed Bayesian MTBF estimating method is better than LSE and suitable for NC machine tools under small samples.

关键词： LSE MTBF NC machine tools small sample Bayes metropolis algorithm

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Singular relaxation of a random walk in a box with a metropolis Monte Carlo dynamics

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JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL 2023年第25期56卷 255003-255003页

作者： Chepelianskii, Alexei D. Majumdar, Satya N. Schawe, Hendrik Trizac, Emmanuel Univ Paris Saclay CNRS Lab Phys Solides F-91405 Orsay France Univ Paris Saclay CNRS LPTMS F-91405 Orsay France CY Cergy Paris Univ LPTM UMR 8089 CNRS F-95000 Cergy France

We study analytically the relaxation eigenmodes of a simple Monte Carlo algorithm, corresponding to a particle in a box which moves by uniform random jumps. Moves outside of the box are rejected. At long times, the system approaches the equilibrium probability density, which is uniform inside the box. We show that the relaxation towards this equilibrium is unusual: for a jump length comparable to the size of the box, the number of relaxation eigenmodes can be surprisingly small, one or two. We provide a complete analytic description of the transition between these two regimes. When only a single relaxation eigenmode is present, a suitable choice of the symmetry of the initial conditions gives a localizing decay to equilibrium. In this case, the deviation from equilibrium concentrates at the edges of the box where the rejection probability is maximal. Finally, in addition to the relaxation analysis of the Master equation, we also describe the full eigen-spectrum of the Master equation including its sub-leading eigen-modes.

关键词： Monte Carlo metropolis algorithm relaxation of Markov chains

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Using Randomized Search algorithms to Estimate HMM Learning Parameters

Using Randomized Search Algorithms to Estimate HMM Learning ...

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IEEE International Advance Computing Conference

作者： Nath, Ravindra Jain, Renu CSJM Univ Dept Comp Sci & Engn UIET Kanpur 208024 Uttar Pradesh India Director Krishna Girls Engn Coll Kanpur 20917 Uttar Pradesh India

ISBN: (纸本)9781424429271

Many complex problems like Speech Recognition, Bioinformatics, Climatology, Control and Communication are solved using Hidden Markov Models (HMM). Mostly, optimization problems are modeled as HMM learning problem in which HMM parameters are either maximized or minimized. In general, Baum-Welch Method (BW) is used to solve HMM learning problem giving only local maxima/minima in exponential time. In this paper, we have modeled HMM learning problem as a discrete optimization problem such that randomized search methods can be used to solve the learning problem. We have implemented metropolis algorithm (MA) and Simulated Annealing algorithm (SAA) to solve the discretized HMM learning problem. A comparative study of randomized algorithms with the Baum Welch method to estimate the HMM learning parameters has been made. The metropolis algorithm is found to reach maxima in minimum number of transactions as compared to the Baum-Welch and Simulated Annealing algorithms.

关键词： Simulated Annealing algorithm metropolis algorithm Baum-Welch Hidden Markov Models

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A Bayes analysis of autoregressive model having functional-coefficients and its application on exchange rate data

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METRON-INTERNATIONAL JOURNAL OF STATISTICS 2024年第3期82卷 363-391页

作者： Tripathi, Praveen Kumar Agarwal, Manika Upadhyay, Satyanshu K. Banasthali Vidyapith Dept Math & Stat Banasthali India Banaras Hindu Univ Dept Stat Varanasi India

The paper provides a Bayes analysis, based on free-knot spline technique, of the popular autoregressive model having functional-coefficients. The model was initially proposed by Chen and Tsay (1993). The technique of polynomial splines of different orders is used to approximate the functional-coefficients. A sample based approach using the Gibbs sampler algorithm with intermediate metropolis steps is adopted to draw the posterior estimates for the parameters involved. Additionally, the technique of reversible jump Markov chain Monte Carlo is incorporated to update the location and number of knots in the polynomial spline. The paper then proceeds with the motive of obtaining both retrospective and prospective predictions based on the selected model. The complete procedure is illustrated by both simulated and a real dataset representing the exchange rate of Indian rupees relative to the US dollars.

关键词： Autoregressive model Exchange rate Functional-coefficients Gibbs sampler Markov chain Monte Carlo metropolis algorithm Polynomial spline Retrospective and prospective predictions Reversible jump

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The Application of Optimisation algorithms in Antenna Array Beampattern Synthesis 26

The Application of Optimisation Algorithms in Antenna Array ...

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26th Irish Signals and Systems Conference (ISSC)

作者： Gorman, Jonathan D. Univ Coll Dublin Sch Elect Elect & Commun Engn Dublin 4 Ireland

ISBN: (纸本)9781467369749

In this paper, optimisation algorithms are successfully applied to a number of antenna array beampattern synthesis problems including, a Uniform Linear Array (ULA), a ULA with an inactive element, and a 2D Wireless Sensor Network (WSN) array. The algorithms presented include a Greedy algorithm (GDA), a metropolis algorithm (MA), and a Genetic algorithm (GA).

关键词： antenna array beampattern synthesis optimisation algorithm uniform linear array wireless sensor network greedy algorithm metropolis algorithm genetic algorithm collaborative beamforming Dolph-Chebyshev internet of things

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Estimating overidentified, nonrecursive, time-varying coefficients structural vector autoregressions

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QUANTITATIVE ECONOMICS 2015年第2期6卷 359-384页

作者： Canova, Fabio Forero, Fernando J. Perez EUI Florence Italy CEPR London England BCRP Boston MA USA

This paper provides a general procedure to estimate structural vector autoregressions. The algorithm can be used in constant or time-varying coefficient models, and in the latter case, the law of motion of the coefficients can be linear or nonlinear. It can deal in a unified way with just-identified (recursive or nonrecursive) or overidentified systems where identification restrictions are of linear or of nonlinear form. We study the transmission of monetary policy shocks in models with time-varying and time-invariant parameters.

关键词： Time-varying coefficient structural VAR models metropolis algorithm identification restrictions monetary transmission mechanism

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3D MC simulation of grain growth kinetics and the Zener limit in polycrystals

3D MC simulation of grain growth kinetics and the Zener limi...

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4th International Conference on Mechanics, Simulation and Control (ICMSC)

作者： Phaneesh, K. R. Bhat, Anirudh Mukherjee, G. Kashyap, K. T. MS Ramaiah Inst Technol Dept Mech Engn Bangalore Karnataka India Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA PES Inst Technol Dept Engn Mech Bangalore Karnataka India

ISBN: (纸本)9783038351795

Large scale Potts model Monte Carlo simulation was carried on 3-dimensional square lattices of 100 3 and 200 3 sizes using the metropolis algorithm to study grain growth behavior. Simulations were carried out to investigate both growth kinetics as well as the Zener limit in two-phase polycrystals inhibited in growth by second phase particles of single-voxel size. Initially the matrices were run to 10,000 Monte Carlo steps (MCS) to check the growth kinetics in both single phase and two-phase poly-crystals. Grain growth exponent values obtained as a result have shown to be highest (similar to 0.4) for mono-phase materials while the value decreases with addition of second phase particles. Subsequently the matrices were run to stagnation in the presence of second phase particles of volume fractions ranging from 0.001 to 0.1. Results obtained have shown a cube root dependence of the limiting grain size over the particle volume fraction thus reinforcing earlier 3D simulation efforts. It was observed that there was not much difference in the values of either growth kinetics or the Zener limit between 100(3) and 200(3) sized matrices, although the results improved mildly with size.

关键词： Monte Carlo simulation metropolis algorithm Monte Carlo steps Grain growth exponent Zener limit Limiting grain size

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